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Abstract

Weak-lensing shear estimates show a troublesome dependence on the apparent brightness of the galaxies used to measure the ellipticity: in several studies, the amplitude of the inferred shear falls sharply with decreasing source significance. This dependence limits the overall ability of upcoming large weak-lensing surveys to constrain cosmological parameters.

We seek to provide a concise overview of the impact of pixel noise on weak-lensing measurements, covering the entire path from noisy images to shear estimates. We show that there are at least three distinct layers, where pixel...

Abstract

Weak-lensing shear estimates show a troublesome dependence on the apparent brightness of the galaxies used to measure the ellipticity: in several studies, the amplitude of the inferred shear falls sharply with decreasing source significance. This dependence limits the overall ability of upcoming large weak-lensing surveys to constrain cosmological parameters.

We seek to provide a concise overview of the impact of pixel noise on weak-lensing measurements, covering the entire path from noisy images to shear estimates. We show that there are at least three distinct layers, where pixel noise not only obscures but also biases the outcome of the measurements: (1) the propagation of pixel noise to the non-linear observable ellipticity; (2) the response of the shape-measurement methods to limited amount of information extractable from noisy images and (3) the reaction of shear estimation statistics to the presence of noise and outliers in the measured ellipticities.

We identify and discuss several fundamental problems and show that each of them is able to introduce biases in the range of a few tens to a few per cent for galaxies with typical significance levels. Furthermore, all of these biases do not only depend on the brightness of galaxies but also depend on their ellipticity, with more elliptical galaxies often being harder to measure correctly. We also discuss existing possibilities to mitigate and novel ideas to avoid the biases induced by pixel noise. We present a new shear estimator that shows a more robust performance for noisy ellipticity samples. Finally, we release the open-source python code to predict and efficiently sample from the noisy ellipticity distribution and the shear estimators used in this work at https://github.com/pmelchior/epsnoise.